E5-base-v2

Text Embeddings by Weakly-Supervised Contrastive Pre-training .梁王，南阳，黄晓龙，焦斌星，杨林军，姜大新，马基达，魏福如，arXiv 2022

此模型共有12层，嵌入大小为768。

用法

以下是从MS-MARCO段落排名数据集中对查询和段落进行编码的示例。

import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]


# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
               'query: summit define',
               "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
               "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."]

tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-base-v2')
model = AutoModel.from_pretrained('intfloat/e5-base-v2')

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())

训练详细信息

请参阅我们在 https://arxiv.org/pdf/2212.03533.pdf 中的论文。

基准评估

查看 unilm/e5 以重新生成 BEIR 和 MTEB benchmark 上的评估结果。

对Sentence Transformers的支持

以下是与sentence_transformers一起使用的示例。

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-base-v2')
input_texts = [
    'query: how much protein should a female eat',
    'query: summit define',
    "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)

软件包要求

pip install sentence_transformers~=2.2.2

贡献者： michaelfeil

常见问题

1. 是否需要在输入文本中添加前缀“query:”和“passage:”？

是的，这是模型的训练方式，否则性能会降低。

以下是一些经验法则：

• 对于非对称任务，例如开放式QA中的段落检索，临时信息检索，应相应地使用“query:”和“passage:”。

• 对于对称任务，例如语义相似性，复述检索，应使用“query:”前缀。

• 如果要将嵌入用作特征，例如线性探测分类，聚类，应使用“query:”前缀。

2. 为什么我重新生成的结果与模型卡中报告的结果略有不同？

不同版本的transformers和pytorch可能会导致微小但不为零的性能差异。

引用

如果您发现我们的论文或模型有帮助，请考虑如下引用：

@article{wang2022text,
  title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
  journal={arXiv preprint arXiv:2212.03533},
  year={2022}
}

限制

此模型仅适用于英文文本。长文本将被截断为最多512个标记。